Fonts have had trends throughout their history, not only in when they were invented but also in their usage and popularity. In this paper, we attempt to specifically find the trends in font usage using robust regression on a large collection of text images. We utilize movie posters as the source of fonts for this task because movie posters can represent time periods by using their release date. In addition, movie posters are documents that are carefully designed and represent a wide range of fonts. To understand the relationship between the fonts of movie posters and time, we use a regression Convolutional Neural Network (CNN) to estimate the release year of a movie using an isolated title text image. Due to the difficulty of the task, we propose to use of a hybrid training regimen that uses a combination of Mean Squared Error (MSE) and Tukey's biweight loss. Furthermore, we perform a thorough analysis on the trends of fonts through time.
翻译:在本文中,我们试图利用大量文本图像的强烈回归来具体发现字体使用趋势。我们利用电影海报作为这一任务的字体来源,因为电影海报可以使用发布日期代表时间段。此外,电影海报是精心设计的文件,代表了广泛的字体。为了理解电影海报和时间的字体之间的关系,我们使用一个回归的 Convolutional Neural网络(CNN)来估计电影的发行年份,使用一个孤立的标题文本图像。由于任务难度,我们提议使用混合培训机制,使用中度偏差(MSE)和Tukey的双体重损失组合。此外,我们通过时间对字体趋势进行彻底分析。